Data-driven causal analysis of observational time series in ecology

Abstract

Complex ecosystems are challenging to understand as they often defy manipulative experiments for practical or ethical reasons. In response, several fields have developed parallel approaches to infer causal relations from observational time series. Yet these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal inference approaches popular in ecological time series analysis: pairwise correlation, Granger causality, and state space reconstruction. For each, we ask what a method tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of causal inference methods, and point out how so-called "model-free" causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of causal inference approaches and encourage explicit statements of assumptions.